Security Incidents Analysis: Global Patterns and Trends

1 Introduction

This analysis explores patterns and trends in global security incidents, identifying hotspots and tracking how they’ve evolved over time. By examining data on security incidents from around the world, we can better understand which regions face the greatest challenges and how these challenges have shifted in recent years.

Security incidents can range from physical attacks to cyber threats, and their distribution isn’t uniform across the globe. Understanding these patterns can help security professionals, policymakers, and researchers allocate resources effectively and develop targeted strategies to mitigate risks.

The following analysis uses data visualization techniques to uncover insights about: - The geographic distribution of security incidents - How incident patterns have changed over time - Which countries represent security hotspots, both historically and recently

1.1 Dataset Overview

Our analysis begins with a cleaned dataset of security incidents collected from various sources. Let’s examine the scope of our data:

Code 
print(f"Dataset contains {len(df)} incidents across {df['country'].nunique()} countries")
print(f"Time period covered: {df['year'].min()} to {df['year'].max()}")
Dataset contains 4314 incidents across 95 countries
Time period covered: 1997 to 2025

This extensive dataset allows us to perform comprehensive analysis across both geographic and temporal dimensions. The data has been preprocessed to ensure consistency in country names, coordinate information, and incident classifications.

2 Global Distribution of Security Incidents

Security incidents aren’t distributed evenly across the world. Some regions experience higher concentrations due to various factors including geopolitical tensions, economic disparities, and historical conflicts. Visualizing this distribution helps us identify global patterns.

2.1 Interactive Global Incident Map

The map below displays incidents across the globe, with colors indicating the severity based on the number of people affected: - Blue: No reported casualties - Green: 1-5 affected individuals - Orange: 6-20 affected individuals - Red: More than 20 affected individuals

Clustering is used to manage dense areas where multiple incidents occurred in close proximity.

Code 
def create_incidents_map(data):
    """
    Create an interactive folium map with clustered markers for security incidents.
    
    Args:
        data: DataFrame containing incident data with latitude and longitude coordinates
        
    Returns:
        folium.Map: Interactive map with clustered markers
    """
    # Calculate center coordinates for the map
    center_lat = data['latitude'].mean()
    center_lon = data['longitude'].mean()
    
    # Create base map
    incidents_map = folium.Map(location=[center_lat, center_lon], zoom_start=2)
    
    # Add marker cluster
    marker_cluster = MarkerCluster().add_to(incidents_map)
    
    # Filter for valid coordinates
    valid_coords = data[data['latitude'].notna() & data['longitude'].notna()]
    
    # Define color function based on number of affected people
    def get_color(affected):
        if pd.isna(affected) or affected == 0:
            return 'blue'
        elif affected <= 5:
            return 'green'
        elif affected <= 20:
            return 'orange'
        else:
            return 'red'
    
    # Add markers for each incident
    for _, row in valid_coords.iterrows():
        popup_text = f"""
        <b>Country:</b> {row['country']}<br>
        <b>Year:</b> {row['year']}<br>
        <b>Total Affected:</b> {row['total_affected']}<br>
        <b>Attack Type:</b> {row['means_of_attack'] if 'means_of_attack' in row and pd.notna(row['means_of_attack']) else 'Unknown'}<br>
        """
        
        folium.CircleMarker(
            location=[row['latitude'], row['longitude']],
            radius=5,
            popup=folium.Popup(popup_text, max_width=300),
            fill=True,
            fill_opacity=0.7,
            color=get_color(row['total_affected']),
            fill_color=get_color(row['total_affected'])
        ).add_to(marker_cluster)
    
    return incidents_map
Code 
# Create and display the global incidents map
global_incidents_map = create_incidents_map(df)
map_filename = "images/global_security_incidents_map.html"
global_incidents_map.save(map_filename)
global_incidents_map
Make this Notebook Trusted to load map: File -> Trust Notebook

The interactive map reveals several important patterns: - Incidents tend to cluster in certain regions, particularly in conflict zones and areas with political instability - Urban centers often show higher concentrations of incidents - The distribution of high-severity incidents (red markers) isn’t uniform, suggesting regional differences in the nature of security threats

You can zoom in on specific regions and click on individual markers to get more details about each incident.

5 Identifying Security Hotspots

Not all countries experience security incidents at the same rate. By identifying which nations have faced the highest numbers of incidents, we can focus attention on areas that may require additional security resources and intervention.

5.1 Countries with Most Incidents: All-Time Analysis

First, let’s look at which countries have experienced the most security incidents over the entire period covered by our dataset:

Code 
# Get total incidents by country for all time
total_by_country = df.groupby('country').size().reset_index(name='total_incidents')
total_by_country = total_by_country.sort_values('total_incidents', ascending=False)
top15_countries = total_by_country.head(15)

# Create bar chart for all-time top countries
fig_top_all_time = px.bar(
    top15_countries,
    x='country',
    y='total_incidents',
    title='Top 15 Countries by Security Incidents (All Time)',
    labels={'total_incidents': 'Number of Incidents', 'country': 'Country'},
    color='total_incidents',
    color_continuous_scale='Viridis',
    height=450
)

# Configure layout
fig_top_all_time.update_layout(
    title={
        'text': 'Top 15 Countries by Security Incidents (All Time)',
        'y': 0.95,
        'x': 0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': {'size': 20}
    },
    xaxis={'categoryorder': 'total descending', 'tickangle': 45},
    coloraxis_showscale=False
)

# Display the figure
fig_top_all_time.show()

# Save figure
fig_top_all_time.write_html("images/top_countries_all_time.html")

This visualization highlights the countries that have historically been most affected by security incidents. Several factors might contribute to a country appearing on this list: - Long-standing regional conflicts - Political instability - Higher population (which can increase the absolute number of incidents) - More comprehensive reporting of incidents

6 Comparing Historical and Recent Hotspots

To better understand how security landscapes have changed, let’s directly compare the countries that appear in our all-time and recent top 15 lists:

Code 
# Find overlapping countries in both top 15 lists
all_time_set = set(top15_countries['country'])
recent_set = set(top15_recent['country'])
matches = all_time_set.intersection(recent_set)

# Print results
print(f"Number of countries appearing in both top 15 lists: {len(matches)}")
print("Countries appearing in both lists:")
for country in sorted(matches):
    print(f"- {country}")

# Countries only in all-time list
all_time_only = all_time_set - recent_set
if all_time_only:
    print("\nCountries in all-time top 15 but not in recent top 15:")
    for country in sorted(all_time_only):
        print(f"- {country}")

# Countries only in recent list
recent_only = recent_set - all_time_set
if recent_only:
    print("\nCountries in recent top 15 but not in all-time top 15:")
    for country in sorted(recent_only):
        print(f"- {country}")
Number of countries appearing in both top 15 lists: 13
Countries appearing in both lists:
- Afghanistan
- Central African Republic
- DR Congo
- Ethiopia
- Mali
- Myanmar
- Nigeria
- Occupied Palestinian Territories
- Somalia
- South Sudan
- Sudan
- Syrian Arab Republic
- Yemen

Countries in all-time top 15 but not in recent top 15:
- Iraq
- Pakistan

Countries in recent top 15 but not in all-time top 15:
- Cameroon
- Haiti

This comparison reveals three important categories:

  1. Persistent Hotspots: Countries appearing in both lists have faced ongoing security challenges throughout the period covered by our dataset. These nations may require sustained international attention and support.

  2. Improving Situations: Countries that appear in the all-time list but not in the recent list have likely experienced security improvements. Understanding the factors behind these positive trends could provide valuable insights for improving security elsewhere.

  3. Emerging Concerns: Countries that appear in the recent list but not in the all-time list represent emerging security challenges. Early intervention in these areas might prevent further deterioration.

7 Conclusion

This analysis has revealed significant patterns in the global distribution of security incidents, both geographically and temporally. Key takeaways include:

  • Security incidents aren’t distributed uniformly across the globe, with clear hotspots in specific regions
  • The global security landscape has evolved considerably over time, with some countries experiencing improvements while others face deteriorating conditions
  • Both historical patterns and recent trends are important when assessing security situations
  • The interactive visualizations presented here allow for exploration of specific regions and time periods of interest

These insights can help inform security policies, resource allocation, and intervention strategies. By understanding where and when security incidents are most likely to occur, stakeholders can work more effectively to mitigate risks and improve global security.

Future work could expand on this analysis by: - Incorporating additional data sources for more comprehensive coverage - Analyzing specific types of security incidents separately - Examining correlations with socioeconomic and political factors - Developing predictive models to forecast emerging security hotspots

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